Justification, margin values, and analysis populations for oncologic noninferiority and equivalence trials: a meta-epidemiological study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Noninferiority and equivalence trials evaluate whether an experimental therapy's effect on the primary endpoint is contained within an acceptable margin compared with standard of care. The reliability and impact of this conclusion, however, is largely dependent on the justification for this design, the choice of margin, and the analysis population used. METHODS: A meta-epidemiological study was performed of phase 3 randomized noninferiority and equivalence oncologic trials registered at ClinicalTrials.gov. Data were extracted from each trial's registration page and primary manuscript. RESULTS: We identified 65 noninferiority and 10 equivalence trials that collectively enrolled 61 632 patients. Of these, 61 (81%) trials demonstrated noninferiority or equivalence. A total of 65 (87%) trials were justified in the use of a noninferiority or equivalence design either because of an inherent advantage (53 trials), a statistically significant quality-of-life improvement (6 trials), or a statistically significant toxicity improvement (6 trials) of the interventional treatment relative to the control arm. Additionally, 69 (92.0%) trials reported a prespecified noninferiority or equivalence margin of which only 23 (33.3%) provided justification for this margin based on prior literature. For trials with time-to-event primary endpoints, the median noninferiority margin was a hazard ratio of 1.22 (range = 1.08-1.52). Investigators reported a per-protocol analysis for the primary endpoint in only 28 (37%) trials. CONCLUSIONS: Although most published noninferiority and equivalence trials have clear justification for their design, few provide rationale for the chosen margin or report a per-protocol analysis. These findings underscore the need for rigorous standards in trial design and reporting.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchMeta-epidemiology (broad) Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | low |
| gpt | MetaresearchMeta-epidemiology (narrow)Meta-epidemiology (broad) Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | medium |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.232 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it